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already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n","Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2022.7.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n","Installing collected packages: xxhash, dill, multiprocess, datasets\n","Successfully installed datasets-2.13.1 dill-0.3.6 multiprocess-0.70.14 xxhash-3.2.0\n","Collecting accelerate\n","  Downloading accelerate-0.21.0-py3-none-any.whl (244 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy>=1.17 in 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accelerate-0.21.0\n"]}],"source":["!pip install transformers==4.28.0\n","!pip install datasets\n","!pip install accelerate -U"]},{"cell_type":"code","source":["!gdown 1a1v06PvGaWmTCWvL33o2C133D90SRzMC # download cw_texts_clean_bert.csv\n","!mkdir output\n","!mkdir /data\n","!mv cw_texts_clean_bert.csv /data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QvRTCsEPvVCY","executionInfo":{"status":"ok","timestamp":1689555754554,"user_tz":420,"elapsed":5410,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"d0a56c28-bccb-484d-8a19-750f575d612e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading...\n","From: https://drive.google.com/uc?id=1a1v06PvGaWmTCWvL33o2C133D90SRzMC\n","To: /content/cw_texts_clean_bert.csv\n","100% 59.5M/59.5M [00:01<00:00, 43.5MB/s]\n"]}]},{"cell_type":"code","source":["# the following lines (177 in total) are directly copied from Haffner et al. (2023), Political Analysis\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from datasets import Dataset\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification\n","from sklearn.metrics import mean_absolute_error\n","from transformers import TrainingArguments, Trainer\n","import torch\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import mean_squared_error\n","from sklearn.metrics import r2_score\n","from numpy.random import seed\n","from tqdm import tqdm\n","seed(1)\n","\n","final = pd.read_csv('/data/cw_texts_clean_bert.csv')\n","\n","print(len(final))\n","final['fatalities_log'] = final['fatalities_log'].astype(float)\n","\n","train = final[final['year'] < 2021]\n","train.reset_index(inplace=True, drop=True)\n","test = final[final['year'] == 2021]\n","test.reset_index(inplace=True, drop=True)\n","\n","train['split_dummy'] = ''\n","train['year'] = train['year'].astype(int)\n","train['month'] = train['month'].astype(int)\n","\n","for i in tqdm(range(len(train))):\n","    if train['year'][i] == 2020 and train['month'][i] > 6:\n","        train['split_dummy'][i] = 1\n","    else:\n","        train['split_dummy'][i] = 0\n","\n","train['split_dummy'].value_counts()\n","\n","train1 = train[train['split_dummy'] == 0]\n","vali = train[train['split_dummy'] == 1]\n","\n","X_train = train1['final_text']\n","y_train = train1['fatalities_log']\n","X_vali = vali['final_text']\n","y_vali = vali['fatalities_log']\n","X_test = test['final_text']\n","y_test = test['fatalities_log']\n","X_train = pd.DataFrame(X_train)\n","X_test = pd.DataFrame(X_test)\n","X_vali = pd.DataFrame(X_vali)\n","y_vali = pd.DataFrame(y_vali)\n","y_train = pd.DataFrame(y_train)\n","y_test = pd.DataFrame(y_test)\n","\n","df_train = pd.concat([X_train, y_train], axis=1)\n","df_val = pd.concat([X_vali, y_vali], axis=1)\n","df_test = pd.concat([X_test, y_test], axis=1)\n","\n","\n","raw_train_ds = Dataset.from_pandas(df_train)\n","raw_val_ds = Dataset.from_pandas(df_val)\n","raw_test_ds = Dataset.from_pandas(df_test)\n","\n","BASE_MODEL = \"snowood1/ConfliBERT-scr-uncased\"\n","LEARNING_RATE = 2e-5 # decreased from 2e-3\n","MAX_LENGTH = 256\n","BATCH_SIZE = 16\n","EPOCHS = 10 # reduced from 20\n","\n","tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)\n","def model_init():\n","    model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL, num_labels=1, ignore_mismatched_sizes=True)\n","    # for param in model.bert.parameters():\n","    #     param.requires_grad = False\n","    return model\n","\n","\n","ds = {\"train\": raw_train_ds, \"validation\": raw_val_ds, \"test\": raw_test_ds}\n","\n","\n","def preprocess_function(examples):\n","    label = examples[\"fatalities_log\"]\n","    examples = tokenizer(examples[\"final_text\"], truncation=True, padding=\"max_length\", max_length=256)\n","\n","    # Change this to real number\n","    examples[\"label\"] = float(label)\n","    return examples\n","\n","\n","for split in ds:\n","    ds[split] = ds[split].map(preprocess_function)\n","\n","\n","def compute_metrics_for_regression(eval_pred):\n","    logits, labels = eval_pred\n","    labels = labels.reshape(-1, 1)\n","\n","    mse = mean_squared_error(labels, logits)\n","    mae = mean_absolute_error(labels, logits)\n","    r2 = r2_score(labels, logits)\n","    single_squared_errors = ((logits - labels).flatten() ** 2).tolist()\n","\n","    # Compute accuracy\n","    # Based on the fact that the rounded score = true score only if |single_squared_errors| < 0.5\n","    accuracy = sum([1 for e in single_squared_errors if e < 0.25]) / len(single_squared_errors)\n","\n","    return {\"mse\": mse, \"mae\": mae, \"r2\": r2, \"accuracy\": accuracy}\n","\n","training_args = TrainingArguments(\n","    output_dir=\"/data/output/conflibert\",\n","    learning_rate=LEARNING_RATE,\n","    per_device_train_batch_size=BATCH_SIZE,\n","    per_device_eval_batch_size=BATCH_SIZE,\n","    num_train_epochs=EPOCHS,\n","    evaluation_strategy=\"steps\",\n","    eval_steps=500,\n","    save_total_limit=10,\n","    load_best_model_at_end=True,\n","    weight_decay=0.01,\n","    seed=123\n",")\n","\n","class RegressionTrainer(Trainer):\n","    def compute_loss(self, model, inputs, return_outputs=False):\n","        labels = inputs.pop(\"labels\")\n","        outputs = model(**inputs)\n","        logits = outputs[0][:, 0]\n","        loss = torch.nn.functional.mse_loss(logits, labels)\n","        return (loss, outputs) if return_outputs else loss\n","\n","\n","trainer = RegressionTrainer(\n","    model_init=model_init,\n","    args=training_args,\n","    train_dataset=ds[\"train\"],\n","    eval_dataset=ds[\"validation\"],\n","    compute_metrics=compute_metrics_for_regression,\n",")\n","\n","trainer.train()\n","\n","for obj in trainer.state.log_history:\n","    print(obj)\n","\n","trainer.eval_dataset=ds[\"test\"]\n","trainer.evaluate()\n","\n","raw_pred_test, _, _ = trainer.predict(ds[\"test\"])\n","print(raw_pred_test[:20])\n","pd.DataFrame(raw_pred_test).to_csv('/data/cw_pred_test_zero.csv')\n","\n","df_results = pd.DataFrame(columns=['Test_MSE','Test_R2'], index=range(0,1))\n","mse = mean_squared_error(ds['test']['fatalities_log'], raw_pred_test)\n","r_squared = r2_score(ds['test']['fatalities_log'], raw_pred_test)\n","print(mse)\n","print(r_squared)\n","df_results['Test_MSE'][0] = mse\n","df_results['Test_R2'][0] = r_squared\n","df_results.to_csv('/data/bert_metrics.csv', index=False)\n","\n","## Whole CW\n","print('Now whole corpus')\n","raw_train1_ds = Dataset.from_pandas(final)\n","ds = {\"test\": raw_train1_ds}\n","\n","## Create dataset\n","\n","for split in ds:\n","    ds[split] = ds[split].map(preprocess_function)\n","\n","print('Now predicting')\n","raw_pred, _, _ = trainer.predict(ds[\"test\"])\n","print(raw_pred[:20])\n","pd.DataFrame(raw_pred).to_csv('/data/cw_bert_pred_zero.csv')\n","\n","print('DF saved')\n","\n","trainer.eval_dataset=ds[\"test\"]\n","trainer.evaluate()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["8b06af587f844de3964338222e964c62","4bd46be67b8545698167a8a6ee02127b","3fca9e2d18764302bd6ed9f42509bf28","7a69c81dfa0a4a66a5b7a1d5ff828b53","d4296fd3b1e64861b9a0c5f365229595","d368ee33b4424241a3dd28ae7bcc7007","1628f7000b71474c910d0a6459c8e16a","1ffd4bdf3078443e8c9ad78eca0ddb33","db44560ed73f4d4d8b0462b172c787ef","75cb7685660f464f909949cb61929777","13d0cccae3f248c7bd672e9d11972029","d3a7cb366d3f4b349ea0248694eb2ed7","bd7c50d885244893b48f0aa3ea70c1f6","31750517dfa549579c832caabf6f1fe8","b9a1b7fafde54688b205635958c8c8d1","3b834342253a4af6b26f3e39419ebccc","dc3a02afbb41422cafa614bba47a0692","607852dff7fd492f9c8e29805c454b77","a07ca5f7da33453aa89d5e72a8aa95bc","fd1fb8ccfdaa46ffaa62877c8f9bd9cb","0185bd38b2ca45119a02950008ead873","68e2c3fec548484b802bf7b73443f24c","a3d2bc2225dc4829baac0f693bfe997f","72127825ccc14e7cbc9f2f5efacae2da","739aef8703da42128c9427dc414f23db","7cf6ec6a2f6b40b7bb20c9dcd70b1f8b","2a6bd982fb1a4be3a34063027956662f","946c1f9bf85e4cbfb8af2ccb8807efff","9a71da7d7e634495bfb53e5fc0426932","4e55503fb0ec430bb496c890ba00811b","6f724cd447db41aeafac14867e3d0f63","78bb95962e764b0baa83b1bf27657be0","1725d74e4cf842a59776f173ac969552","c0325dd517a645009c9e7c21bd1c2c22","b21bacc6850f474e898fb0e49f173a89","6e10932be2d8499b90d6600ae2e00215","dc2d74afc85f4fb6a8cef027ccb7f673","da35acbfdc234ad1b14c5c0a92b574e4","5abfdc379ad446d2bf2bb00c64d63c15","ebe9fa65806e476ca31fce767d52e307","13a75d48e8f640ed91010b7882328695","0066c70acead4494aef1f79b913cbb05","741675dba7f246a9ae8fc33414d569d2","7a52c7c77a114c9186ba41967f355a9b","f938d469c3f44775b6904a249f74c123","e72affd880054a67bfe35dc6bf1ae5a4","c689c7832c1548b29a986e7aa15982de","f100145fabc4481094c47ddeea81349d","1779814e9ce54ea0a3f0811868b83f5f","898c7fdc84214e0c83b1ae41e28ba057","40c7ca67cbcd4451ae266df29450b89c","143063bc80954d60a8748b5195221c5f","2f2ea68c7f65456a82db70141654b10e","574dd338b8784e709a3de878b467e653","51f69447f9514f7ab0bbe5c007f2fddd","42cfa0df14444deb85423868ac8891df","f1461d7bae234b55ba80ecda52e15352","4a087a555fad42a695735957c7fd229b","081c4d156d0b4229b484ccbc142f0912","b3d51f0374094761968f3e0bb8a61791","9493a7d4933e4d0eab6bd777adef8fb6","86201ce7afbe47f5b16f7fd0b83be57b","32223a562d6f4fd1a482cbb00380e9cd","35d29d2be8bf4b2eaa3cc13c524af928","df79a9895d8541758e2bf3a53451decd","10a044f8c8394163963bc540b5c9ee21","bff9afb45ede4c7c96fcd6c8f5e0009f","b17ae7a3792344f298509085770408fd","1072ec27f24c4e639b196b7b4d41e602","a45b26f598d44372ad7bb7ad0b6478f9","121d21b61dd342fda70d6168e08b6f68","59035b7e625d4009adb28d7d29d005da","ba07cf057235454f8bcecb55764fa855","b6a0f73c7f484f68847da97bbd519f6d","7ae74e9ed00f48889ac22b42db8d43ae","5de2c22956404bcc81ac26960675407c","92985c3b29164a9d9423de8290ec5a52","1c480dabd46948d1ae54c1538e7642a2","5d54163af6dc41389bff5fc25d7bd9bb","4bfa6b25ec934f73a89b5708df79f683","96f8dad9fb7442d3b4227180d3b450d0","6966c7aae7f347ac9ec014968c59616b","47da220a12ee4b0983bcf252acd7a0e7","e5ba15c3ec864a8cb19a0d5c5150bd34","d2d842fb49904fd8826e09e1b59a4fe4","b293b5e4b7bb4b9aba9346dcf90200cc","d4f1c772ec924168a67702048b8ff72e","ccaeb1f5a8fd4ef5b8832fc1ef21d008","584e665fb9564771830bfd843d33cd60","dfc825b3acf9416484a297dc01175207","c10bf285b0fe458a803868441aeba2bc","2af6cd6ddec649dab941f11e219c9963","0d70cee3dc0d4e46b529c7578df3d777","f62175e003fe4e59855d85be4a814b66","7a4f8b2feaec4fbe9c116cc5b9c48a0f","53c177f1066245efaca146bac8847bbd","ab070db2346a485c9b08b1adc79f80c9","b30fc3483fc04cc3a1be04dad9d4eac2","1983f2176e2c4547b60ec788d23de8e9","f90e04675c294221841e6671ea0cc811","15aedde70d784f61add0caa770e32455","3c0d5b0ccf4c428ca901b11575d61a5e","18553d8306754027b202df7390e57046","0614f00363644487ada65237905c9090","6c1c27a87a2f4e28afdbcc360ce251fb","971ff2e7c39046fe84d8340578d13493","d7e9b2fee7a54f4b832c3efa8900736d","b32e9c60ca154853a0ea808420c514fe","a4553067ec7e4403997c51945b53b95a","d6b14cc902794329a45cf7bc110de634"]},"id":"_UUtlbRAySQ_","outputId":"8bdbcacd-1363-4a89-ed20-ff32dc5ff8c5","executionInfo":{"status":"ok","timestamp":1689558720216,"user_tz":420,"elapsed":2965665,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-3-bebe427d4d62>:26: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'] = ''\n","<ipython-input-3-bebe427d4d62>:27: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['year'] = train['year'].astype(int)\n","<ipython-input-3-bebe427d4d62>:28: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['month'] = train['month'].astype(int)\n"]},{"output_type":"stream","name":"stdout","text":["23868\n"]},{"output_type":"stream","name":"stderr","text":["  0%|          | 0/22572 [00:00<?, ?it/s]<ipython-input-3-bebe427d4d62>:34: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 0\n","<ipython-input-3-bebe427d4d62>:32: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 1\n","100%|██████████| 22572/22572 [00:06<00:00, 3231.08it/s]\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/270 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8b06af587f844de3964338222e964c62"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d3a7cb366d3f4b349ea0248694eb2ed7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/224k [00:00<?, 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model checkpoint at snowood1/ConfliBERT-scr-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.bias', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at snowood1/ConfliBERT-scr-uncased and are newly initialized: ['classifier.weight', 'bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","Some weights of the model checkpoint at snowood1/ConfliBERT-scr-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.bias', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at snowood1/ConfliBERT-scr-uncased and are newly initialized: ['classifier.weight', 'bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='13710' max='13710' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [13710/13710 45:06, Epoch 10/10]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Mse</th>\n","      <th>Mae</th>\n","      <th>R2</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>500</td>\n","      <td>1.359400</td>\n","      <td>1.229399</td>\n","      <td>1.229399</td>\n","      <td>0.631700</td>\n","      <td>0.686137</td>\n","      <td>0.682099</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>0.899100</td>\n","      <td>1.234646</td>\n","      <td>1.234646</td>\n","      <td>0.641333</td>\n","      <td>0.684797</td>\n","      <td>0.683642</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>0.834900</td>\n","      <td>1.185796</td>\n","      <td>1.185796</td>\n","      <td>0.607861</td>\n","      <td>0.697269</td>\n","      <td>0.694444</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>0.641600</td>\n","      <td>1.108420</td>\n","      <td>1.108419</td>\n","      <td>0.613219</td>\n","      <td>0.717023</td>\n","      <td>0.700617</td>\n","    </tr>\n","    <tr>\n","      <td>2500</td>\n","      <td>0.665500</td>\n","      <td>0.940565</td>\n","      <td>0.940565</td>\n","      <td>0.504655</td>\n","      <td>0.759876</td>\n","      <td>0.716049</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>0.629600</td>\n","      <td>1.203513</td>\n","      <td>1.203513</td>\n","      <td>0.595329</td>\n","      <td>0.692746</td>\n","      <td>0.688272</td>\n","    </tr>\n","    <tr>\n","      <td>3500</td>\n","      <td>0.571200</td>\n","      <td>1.079722</td>\n","      <td>1.079722</td>\n","      <td>0.526228</td>\n","      <td>0.724349</td>\n","      <td>0.716049</td>\n","    </tr>\n","    <tr>\n","      <td>4000</td>\n","      <td>0.511100</td>\n","      <td>1.022602</td>\n","      <td>1.022602</td>\n","      <td>0.539213</td>\n","      <td>0.738932</td>\n","      <td>0.709877</td>\n","    </tr>\n","    <tr>\n","      <td>4500</td>\n","      <td>0.432400</td>\n","      <td>1.012636</td>\n","      <td>1.012636</td>\n","      <td>0.646065</td>\n","      <td>0.741476</td>\n","      <td>0.686728</td>\n","    </tr>\n","    <tr>\n","      <td>5000</td>\n","      <td>0.476700</td>\n","      <td>0.927344</td>\n","      <td>0.927344</td>\n","      <td>0.507842</td>\n","      <td>0.763251</td>\n","      <td>0.723765</td>\n","    </tr>\n","    <tr>\n","      <td>5500</td>\n","      <td>0.461700</td>\n","      <td>1.025988</td>\n","      <td>1.025988</td>\n","      <td>0.508442</td>\n","      <td>0.738067</td>\n","      <td>0.716049</td>\n","    </tr>\n","    <tr>\n","      <td>6000</td>\n","      <td>0.391000</td>\n","      <td>0.961886</td>\n","      <td>0.961886</td>\n","      <td>0.525181</td>\n","      <td>0.754432</td>\n","      <td>0.726852</td>\n","    </tr>\n","    <tr>\n","      <td>6500</td>\n","      <td>0.383200</td>\n","      <td>0.884101</td>\n","      <td>0.884101</td>\n","      <td>0.454143</td>\n","      <td>0.774291</td>\n","      <td>0.736111</td>\n","    </tr>\n","    <tr>\n","      <td>7000</td>\n","      <td>0.376400</td>\n","      <td>0.992012</td>\n","      <td>0.992012</td>\n","      <td>0.493669</td>\n","      <td>0.746741</td>\n","      <td>0.731481</td>\n","    </tr>\n","    <tr>\n","      <td>7500</td>\n","      <td>0.333000</td>\n","      <td>0.867855</td>\n","      <td>0.867855</td>\n","      <td>0.450554</td>\n","      <td>0.778438</td>\n","      <td>0.737654</td>\n","    </tr>\n","    <tr>\n","      <td>8000</td>\n","      <td>0.337500</td>\n","      <td>0.863027</td>\n","      <td>0.863027</td>\n","      <td>0.454218</td>\n","      <td>0.779671</td>\n","      <td>0.716049</td>\n","    </tr>\n","    <tr>\n","      <td>8500</td>\n","      <td>0.326100</td>\n","      <td>0.912673</td>\n","      <td>0.912673</td>\n","      <td>0.496019</td>\n","      <td>0.766997</td>\n","      <td>0.725309</td>\n","    </tr>\n","    <tr>\n","      <td>9000</td>\n","      <td>0.310000</td>\n","      <td>0.905482</td>\n","      <td>0.905482</td>\n","      <td>0.460075</td>\n","      <td>0.768832</td>\n","      <td>0.726852</td>\n","    </tr>\n","    <tr>\n","      <td>9500</td>\n","      <td>0.301500</td>\n","      <td>0.952885</td>\n","      <td>0.952885</td>\n","      <td>0.484917</td>\n","      <td>0.756730</td>\n","      <td>0.731481</td>\n","    </tr>\n","    <tr>\n","      <td>10000</td>\n","      <td>0.302600</td>\n","      <td>0.908793</td>\n","      <td>0.908792</td>\n","      <td>0.481372</td>\n","      <td>0.767987</td>\n","      <td>0.729938</td>\n","    </tr>\n","    <tr>\n","      <td>10500</td>\n","      <td>0.277100</td>\n","      <td>0.936584</td>\n","      <td>0.936584</td>\n","      <td>0.468717</td>\n","      <td>0.760892</td>\n","      <td>0.746914</td>\n","    </tr>\n","    <tr>\n","      <td>11000</td>\n","      <td>0.288100</td>\n","      <td>0.906395</td>\n","      <td>0.906395</td>\n","      <td>0.472345</td>\n","      <td>0.768599</td>\n","      <td>0.736111</td>\n","    </tr>\n","    <tr>\n","      <td>11500</td>\n","      <td>0.275000</td>\n","      <td>0.929983</td>\n","      <td>0.929983</td>\n","      <td>0.475119</td>\n","      <td>0.762577</td>\n","      <td>0.734568</td>\n","    </tr>\n","    <tr>\n","      <td>12000</td>\n","      <td>0.270400</td>\n","      <td>0.904671</td>\n","      <td>0.904671</td>\n","      <td>0.483562</td>\n","      <td>0.769039</td>\n","      <td>0.729938</td>\n","    </tr>\n","    <tr>\n","      <td>12500</td>\n","      <td>0.260000</td>\n","      <td>0.894465</td>\n","      <td>0.894464</td>\n","      <td>0.457643</td>\n","      <td>0.771645</td>\n","      <td>0.736111</td>\n","    </tr>\n","    <tr>\n","      <td>13000</td>\n","      <td>0.241900</td>\n","      <td>0.896981</td>\n","      <td>0.896981</td>\n","      <td>0.456240</td>\n","      <td>0.771003</td>\n","      <td>0.742284</td>\n","    </tr>\n","    <tr>\n","      <td>13500</td>\n","      <td>0.278000</td>\n","      <td>0.905013</td>\n","      <td>0.905013</td>\n","      <td>0.467941</td>\n","      <td>0.768952</td>\n","      <td>0.746914</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["{'loss': 1.3594, 'learning_rate': 1.927060539752006e-05, 'epoch': 0.36, 'step': 500}\n","{'eval_loss': 1.2293992042541504, 'eval_mse': 1.2293994426727295, 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[-2.0768796e-03]]\n","0.9905870703083635\n","0.7738313664183092\n","Now whole corpus\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/23868 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f90e04675c294221841e6671ea0cc811"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Now predicting\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[[5.159592 ]\n"," [4.063893 ]\n"," [5.1474705]\n"," [3.5846686]\n"," [4.1017876]\n"," [4.2781286]\n"," [2.9331725]\n"," [4.7938433]\n"," [3.8767238]\n"," [4.1029167]\n"," [4.680548 ]\n"," [4.0546117]\n"," [5.2468443]\n"," [4.932831 ]\n"," [3.683504 ]\n"," [3.6262352]\n"," [4.31688  ]\n"," [3.6723454]\n"," [2.6770144]\n"," [3.9926126]]\n","DF saved\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1492' max='1492' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1492/1492 01:31]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'eval_loss': 0.3389165997505188,\n"," 'eval_mse': 0.3389166295528412,\n"," 'eval_mae': 0.23716652393341064,\n"," 'eval_r2': 0.9035855684892002,\n"," 'eval_accuracy': 0.8782051282051282,\n"," 'eval_runtime': 91.2774,\n"," 'eval_samples_per_second': 261.489,\n"," 'eval_steps_per_second': 16.346,\n"," 'epoch': 10.0}"]},"metadata":{},"execution_count":3}]}]}